UAI 2023

243 papers

Human-in-the-Loop Mixup

Katherine M. Collins, Umang Bhatt, Weiyang Liu, Vihari Piratla, Ilia Sucholutsky

Expectation consistency for calibration of neural networks

Lucas Clarte, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová

Establishing Markov equivalence in cyclic directed graphs

Tom Claassen, Joris M. Mooij

Finite-sample guarantees for Nash Q-learning with linear function approximation

Pedro Cisneros-Velarde, Sanmi Koyejo

Parity calibration

Youngseog Chung, Aaron Rumack, Chirag Gupta

Combinatorial categorized bandits with expert rankings

Sayak Ray Chowdhury, Gaurav Sinha, Nagarajan Natarajan, Amit Sharma

Adaptivity Complexity for Causal Graph Discovery

Davin Choo, Kirankumar Shiragur

Enhancing Treatment Effect Estimation: A Model Robust Approach Integrating Randomized Experiments and External Controls using the Double Penalty Integration Estimator

Yuwen Cheng, Lili Wu, Shu Yang

Detection of Short-Term Temporal Dependencies in Hawkes Processes with Heterogeneous Background Dynamics

Yu Chen, Fengpei Li, Anderson Schneider, Yuriy Nevmyvaka, Asohan Amarasingham

Causal inference with outcome-dependent missingness and self-censoring

Jacob M. Chen, Daniel Malinsky, Rohit Bhattacharya

Differential Privacy in Cooperative Multiagent Planning

Bo Chen, Calvin Hawkins, Mustafa O. Karabag, Cyrus Neary, Matthew Hale

MFA: Multi-layer Feature-aware Attack for Object Detection

Wen Chen, Yushan Zhang, Zhiheng Li, Yuehuan Wang

An effective negotiating agent framework based on deep offline reinforcement learning

Siqi Chen, Jianing Zhao, Gerhard Weiss, Ran Su, Kaiyou Lei

Benign Overfitting in Adversarially Robust Linear Classification

Jinghui Chen, Yuan Cao, Quanquan Gu

Modified Retrace for Off-Policy Temporal Difference Learning

Xingguo Chen, Xingzhou Ma, Yang Li, Guang Yang, Shangdong Yang

Learning in online MDPs: is there a price for handling the communicating case?

Gautam Chandrasekaran, Ambuj Tewari

Scalable nonparametric Bayesian learning for dynamic velocity fields

Sunrit Chakraborty, Aritra Guha, Rayleigh Lei, Xuanlong Nguyen

Human Control: Definitions and Algorithms

Ryan Carey, Tom Everitt

Scaling integer arithmetic in probabilistic programs

William X. Cao, Poorva Garg, Ryan Tjoa, Steven Holtzen, Todd Millstein

Overcoming Language Priors for Visual Question Answering via Loss Rebalancing Label and Global Context

Runlin Cao, Zhixin Li

Testing conventional wisdom (of the crowd)

Noah Burrell, Grant Schoenebeck

Inference for mark-censored temporal point processes

Alex Boyd, Yuxin Chang, Stephan Mandt, Padhraic Smyth

Approximating probabilistic explanations via supermodular minimization

Louènas Bounia, Frédéric Koriche

Efficient Learning of Minimax Risk Classifiers in High Dimensions

Kartheek Bondugula, Santiago Mazuelas, Aritz Pérez

Correcting for selection bias and missing response in regression using privileged information

P Boeken, Noud de Kroon, Mathijs de Jong, Joris M. Mooij, Onno Zoeter

Amortized Inference for Gaussian Process Hyperparameters of Structured Kernels

Matthias Bitzer, Mona Meister, Christoph Zimmer

BeliefPPG: Uncertainty-aware heart rate estimation from PPG signals via belief propagation

Valentin Bieri, Paul Streli, Berken Utku Demirel, Christian Holz

Best arm identification in rare events

Anirban Bhattacharjee, Sushant Vijayan, Sandeep Juneja

Inference of a rumor’s source in the independent cascade model

Petra Berenbrink, Max Hahn-Klimroth, Dominik Kaaser, Lena Krieg, Malin Rau

Learning Choice Functions with Gaussian Processes

Alessio Benavoli, Dario Azzimonti, Dario Piga

Sample Boosting Algorithm (SamBA) - An interpretable greedy ensemble classifier based on local expertise for fat data

Baptiste Bauvin, Cécile Capponi, Florence Clerc, Pascal Germain, Sokol Koço

Do we become wiser with time? On causal equivalence with tiered background knowledge

Christine W. Bang, Vanessa Didelez

Neural tangent kernel at initialization: linear width suffices

Arindam Banerjee, Pedro Cisneros-Velarde, Libin Zhu, Mikhail Belkin

Bayesian inference approach for entropy regularized reinforcement learning with stochastic dynamics

Argenis Arriojas, Jacob Adamczyk, Stas Tiomkin, Rahul V. Kulkarni

Quantifying lottery tickets under label noise: accuracy, calibration, and complexity

Viplove Arora, Daniele Irto, Sebastian Goldt, Guido Sanguinetti

Two Sides of Miscalibration: Identifying Over and Under-Confidence Prediction for Network Calibration

Shuang Ao, Stefan Rueger, Advaith Siddharthan

Robust Gaussian process regression with the trimmed marginal likelihood

Daniel Andrade, Akiko Takeda

Transfer learning for individual treatment effect estimation

Ahmed Aloui, Juncheng Dong, Cat P Le, Vahid Tarokh

FLASH: Automating federated learning using CASH

Md I. I. Alam, Koushik Kar, Theodoros Salonidis, Horst Samulowitz

A decoder suffices for query-adaptive variational inference

Sakshi Agarwal, Gabriel Hope, Ali Younis, Erik B. Sudderth

Bounding the optimal value function in compositional reinforcement learning

Jacob Adamczyk, Volodymyr Makarenko, Argenis Arriojas, Stas Tiomkin, Rahul V. Kulkarni

Guided Deep Kernel Learning

Idan Achituve, Gal Chechik, Ethan Fetaya

SubMix: Learning to Mix Graph Sampling Heuristics

Sami Abu-El-Haija, Joshua V. Dillon, Bahare Fatemi, Kyriakos Axiotis, Neslihan Bulut